RegNet: Self-Regulated Network for Image Classification

RegNet: Self-Regulated Network for Image Classification

The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple shortcut connection mechanism limits the ability of re-exploring new potentially complementary features due to the additive function.To address this issue, in this paper, we propose to introduce a regulator module as a memory mechanism to extract complementary features, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional RNNs (e.g., Convolutional LSTMs or Convolutional GRUs), which are shown to be good at extracting Spatio-temporal information. We named the new regulated networks as RegNet. The regulator module can be easily implemented and appended to any ResNet architecture. We also apply the regulator module for improving the Squeeze-and-Excitation ResNet to show the generalization ability of our method. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, SE-ResNet, and other state-of-the-art architectures.

RegNet：用于图像分类的自调节网络

ResNet及其变体在各种计算机视觉任务中均取得了非凡的成功。尽管在使梯度流过构建块方面取得了成功，但由于添加功能，简单的快捷连接机制仍然限制了重新探索新的潜在互补特征的能力。.. 为了解决这个问题，在本文中，我们建议引入一个调节器模块作为一种内存机制来提取互补特征，这些特征将进一步馈送到ResNet。尤其是，调节器模块由卷积RNN（例如，卷积LSTM或卷积GRU）组成，这些卷积RNN显示出能够很好地提取时空信息。我们将新的受监管网络命名为RegNet。调节器模块可以轻松实现并附加到任何ResNet架构中。我们还应用了调节器模块来改进“挤压和激励ResNet”，以展示我们方法的泛化能力。在三个图像分类数据集上的实验结果证明了与标准ResNet，SE-ResNet和其他最新体系结构相比，所提出体系结构的性能令人鼓舞。 （阅读更多）